# creat and draw a graph (network)
import networkx as nx
import matplotlib.pyplot as plt
import plotly.express as px
# load data
import pandas as pd
import numpy as np

# Load datas
df_raw = pd.read_csv("CafeF.HSX.Upto13.11.2023.csv")
df_raw.columns = ["Ticker",'TradingDate', 'Open', 'High', 'Low', 'Close','Total Trade Quantity']

df_raw['TradingDate'] = pd.to_datetime(df_raw['TradingDate'],format='%Y%m%d')

df_stock = df_raw.pivot_table( index='TradingDate',columns='Ticker', values ='Close').loc["2022-01-01":"2023-11-13"]

df_stock = df_stock.dropna(subset=['VNAll-INDEX'])

df_stock =df_stock.dropna(axis=1)
# tách vnindex
vnindex= pd.DataFrame(index=df_stock.index, columns=["Close"], data = df_stock["VNAll-INDEX"].values)
# xóa cột vnallndex
data = df_stock.drop(["VNAll-INDEX"], axis=1)
data

date  = '2023-10-13'
data_train = data.loc['2022-01-01':date]
data_test = data.loc[date:'2023-11-13']
vnindex_train = vnindex.loc['2022-01-01':date]
vnindex_test =  vnindex.loc[date:'2023-11-13' ]

print("Train:",data_train.shape)
print("Test:",data_test.shape)

Train: (433, 253)
Test: (22, 253)

# Correlation matrix
corr = data_train.corr()
corr

#The node size is proportional to the degree
def get_opption(degrees, scale = 200):
    nodes_size = [10+item*scale for item in degrees]
    nodes_color = [item for item in degrees]
    options = {
    "font_size": 20,
    "node_size": nodes_size,
    "node_color": nodes_color,
    "edgecolors": "black",
    "linewidths": 0.2,
    "width": 0.4,
    "alpha":0.6
    }
    return options

# Tính khoảng cách
distance_matrix = np.sqrt(2*(1-corr))
distance_matrix

# crete graph from matrix
d = distance_matrix
G_stock = nx.from_pandas_adjacency(d)
print(G_stock)

Graph with 253 nodes and 31878 edges

#draw the undirected Graph
figure = plt.figure(figsize=(22, 10))
plt.title("Stock")
nx.draw_networkx(G_stock, with_labels=False)
plt.show()

T = nx.minimum_spanning_tree(G_stock)

plt.figure(figsize=(20,15))
plt.title("Stock")

cmap=plt.cm.BrBG
#draw the undirected Graph
pos= nx.spring_layout(T) #Adjust the distance of nodes
nx.draw_networkx(T,
                 pos=pos,
                with_labels=True,
                node_color='lightgreen',
                node_size=600, edge_color='black',
                linewidths=1, font_size=8, alpha =1)
plt.show();

degree_centrality = nx.degree_centrality(T)
closeness_centrality = nx.closeness_centrality(T)
betweenness_centrality = nx.betweenness_centrality(T)
eigenvector_centrality=nx.eigenvector_centrality_numpy(T)

keys = []
values = []

for key, value in degree_centrality.items():
    keys.append(key)
    values.append(value)

dc_data = pd.DataFrame({'stocks': keys, 'degree_centrality': values}).sort_values('degree_centrality', ascending=False)
px.bar(data_frame=dc_data, x='stocks', y='degree_centrality', template='plotly_dark')

keys = []
values = []

for key, value in closeness_centrality.items():
    keys.append(key)
    values.append(value)

cc_data = pd.DataFrame({'stocks': keys, 'closeness_centrality': values}).sort_values('closeness_centrality',
                                                                                       ascending=False)
px.bar(data_frame=cc_data, x='stocks', y='closeness_centrality', template='plotly_dark')

keys = []
values = []

for key, value in betweenness_centrality.items():
    keys.append(key)
    values.append(value)

bc_data = pd.DataFrame({'stocks': keys, 'betweenness_centrality': values}).sort_values('betweenness_centrality',
                                                                                       ascending=False)
px.bar(data_frame=bc_data, x='stocks', y='betweenness_centrality', template='plotly_dark')

keys = []
values = []

for key, value in eigenvector_centrality.items():
    keys.append(key)
    values.append(value)

bc_data = pd.DataFrame({'stocks': keys, 'eigenvector_centrality': values}).sort_values('eigenvector_centrality',
                                                                                       ascending=False)
px.bar(data_frame=bc_data, x='stocks', y='eigenvector_centrality', template='plotly_dark')

# Tính toán ma trận khoảng cách ngắn nhất
shortest_paths = dict(nx.all_pairs_dijkstra_path_length(T))

# Chuyển kết quả thành DataFrame
df_path = pd.DataFrame(shortest_paths)
df_path

import scipy.cluster.hierarchy as shc
plt.figure(figsize=(18, 7))
plt.title("Dendrograms")

dend = shc.dendrogram(shc.linkage(df_path, method='ward'), color_threshold=-np.inf)
plt.axhline(y=-np.inf, c='blue');

plt.figure(figsize=(18, 7))
plt.title("Dendrograms")
dend = shc.dendrogram(shc.linkage(df_path, method='ward'), color_threshold=-np.inf)
plt.axhline(y=-np.inf, c='blue')
plt.axhline(y=140, color='black', linestyle='--');

from sklearn.cluster import AgglomerativeClustering
clusters = AgglomerativeClustering(n_clusters=5, metric='euclidean', linkage='ward')
labels = clusters.fit_predict(df_path)
labels

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 3, 0, 0, 0, 0, 3, 0, 0,
       0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0,
       3, 0, 4, 0, 0, 0, 3, 0, 3, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3,
       3, 0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 3, 3, 3,
       4, 4, 4, 4, 4, 0, 0, 0, 3, 0, 0, 0, 3, 3, 3, 0, 4, 4, 4, 4, 4, 4,
       0, 3, 4, 3, 0, 0, 0, 0, 4, 4, 4, 3, 4, 4, 4, 3, 3, 4, 3, 4, 3, 3,
       4, 4, 4, 3, 4, 4, 4, 1, 4, 4, 1, 1, 4, 1, 1, 1, 1, 3, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

import seaborn as sns
ax=sns.countplot(x=labels)
ax.bar_label(ax.containers[0]);

plt.figure(figsize=(22,15))

# Vẽ đồ thị và tô màu các cụm
pos = nx.spring_layout(T)
nx.draw_networkx(T,
                 pos=pos,
                with_labels=True,
                node_color=labels,
                cmap='Set1',
                node_size=600, edge_color='black',
                linewidths=1, font_size=8, alpha =1 )
plt.show()

# Tính returns
def get_returns(df):
    return np.log(df/df.shift(1)).dropna()

def get_clusters(data,label):
    stocks = data.columns
    return [stocks[label == name] for name in np.unique(label)]

def get_portflolio(cluster,returns, k=2):
    # mỗi cụm lấy k asset có sharpe lớn nhất
    portflolio = []
    for clus in cluster:
        portflolio += list((sharpe_ratio(returns[clus])).nlargest(k).index)
    return portflolio

risk_free_rate = 0.03/365
def sharpe_ratio(returns,rf =risk_free_rate):
    return (returns.mean()-rf) / (returns.std() * np.sqrt(252))

# chia các cổ phiếu theo cụm đã tính toán ở mục 3
clusters = get_clusters(data_train,labels)
clusters

[Index(['AAA', 'AAT', 'ABS', 'ACB', 'ACC', 'ACL', 'ADS', 'AGG', 'AGR', 'ANV',
        ...
        'PHC', 'PLP', 'PLX', 'PNJ', 'PSH', 'QCG', 'SAM', 'SBT', 'SBV', 'SCR'],
       dtype='object', name='Ticker', length=138),
 Index(['TCT', 'TDH', 'TDM', 'THG', 'TIP', 'TLD', 'TLG', 'TN1', 'TNA', 'TNH',
        'TNI', 'TNT', 'TPB', 'TSC', 'TTA', 'TTF', 'TV2', 'TVB', 'TVS', 'VCB',
        'VCG', 'VCI', 'VDS', 'VGC', 'VHC', 'VHM', 'VIB', 'VIC', 'VIP', 'VND'],
       dtype='object', name='Ticker'),
 Index(['VIX', 'VJC', 'VNE', 'VNM', 'VOS', 'VPB', 'VPG', 'VPH', 'VPI', 'VRE',
        'VSC', 'VSH', 'VTO', 'YEG'],
       dtype='object', name='Ticker'),
 Index(['CHP', 'DGW', 'DLG', 'DRH', 'FIT', 'FMC', 'FPT', 'FTS', 'GIL', 'HAX',
        'HCM', 'HDG', 'HHP', 'HHS', 'HT1', 'ICT', 'IJC', 'ITA', 'KHG', 'KSB',
        'LCG', 'LPB', 'NHA', 'NT2', 'NTL', 'NVL', 'PHR', 'POM', 'POW', 'PPC',
        'RAL', 'SAB', 'SHB', 'SKG', 'SMC', 'SSI', 'STK', 'SVD', 'TCH', 'TLH'],
       dtype='object', name='Ticker'),
 Index(['HUB', 'MBB', 'OCB', 'OGC', 'ORS', 'PAN', 'PC1', 'PTB', 'PTC', 'PTL',
        'PVD', 'PVT', 'QBS', 'REE', 'SCS', 'SGR', 'SHA', 'SHI', 'SJD', 'SJS',
        'SSB', 'STB', 'SZC', 'TCB', 'TCD', 'TCL', 'TCM', 'TCO', 'TDC', 'TDG',
        'TEG'],
       dtype='object', name='Ticker')]

returns_train = get_returns(data_train)
# Chọn danh mục đầu tư cổ phiếu (mỗi cluster chọn 2 cổ phiếu)
portfolios = get_portflolio(clusters,returns_train)
portfolios

['BMP', 'CTF', 'VCB', 'TDM', 'VSH', 'VPI', 'FPT', 'CHP', 'TCL', 'REE']

!pip install PyPortfoliOopt
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns

Requirement already satisfied: PyPortfoliOopt in /usr/local/lib/python3.10/dist-packages (1.5.5)
Requirement already satisfied: cvxpy<2.0.0,>=1.1.19 in /usr/local/lib/python3.10/dist-packages (from PyPortfoliOopt) (1.3.2)
Requirement already satisfied: numpy<2.0.0,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from PyPortfoliOopt) (1.23.5)
Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.10/dist-packages (from PyPortfoliOopt) (1.5.3)
Requirement already satisfied: scipy<2.0,>=1.3 in /usr/local/lib/python3.10/dist-packages (from PyPortfoliOopt) (1.11.3)
Requirement already satisfied: osqp>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.19->PyPortfoliOopt) (0.6.2.post8)
Requirement already satisfied: ecos>=2 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.19->PyPortfoliOopt) (2.0.12)
Requirement already satisfied: scs>=1.1.6 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.19->PyPortfoliOopt) (3.2.4)
Requirement already satisfied: setuptools>65.5.1 in /usr/local/lib/python3.10/dist-packages (from cvxpy<2.0.0,>=1.1.19->PyPortfoliOopt) (67.7.2)
Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->PyPortfoliOopt) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->PyPortfoliOopt) (2023.3.post1)
Requirement already satisfied: qdldl in /usr/local/lib/python3.10/dist-packages (from osqp>=0.4.1->cvxpy<2.0.0,>=1.1.19->PyPortfoliOopt) (0.1.7.post0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.19->PyPortfoliOopt) (1.16.0)

New_portfolio = data_train[portfolios]
New_portfolio
mu = expected_returns.mean_historical_return(New_portfolio) #expected returns
S = risk_models.sample_cov(New_portfolio)
# Optimizing for maximal Sharpe ratio
ef = EfficientFrontier(mu, S) # Providing expected returns and covariance matrix as input
weights = ef.max_sharpe() # Optimizing weights for Sharpe ratio maximization

clean_weights = ef.clean_weights() # clean_weights rounds the weights and clips near-zeros

# Printing optimized weights and expected performance for portfolio
clean_weights

OrderedDict([('BMP', 0.25091),
             ('CTF', 0.33426),
             ('VCB', 0.09253),
             ('TDM', 0.0),
             ('VSH', 0.09441),
             ('VPI', 0.0),
             ('FPT', 0.10133),
             ('CHP', 0.12657),
             ('TCL', 0.0),
             ('REE', 0.0)])

# Creating new portfolio with optimized weights
w = list(clean_weights.values())
w
# Visualizing daily returns
returns_port_train =pd.DataFrame(columns=["portfolios"],
                                 data=returns_train[portfolios]@w)
returns_port_train

returns_test = get_returns(data_test)
return_vn_train = get_returns(vnindex_train)
return_vn_test =  get_returns(vnindex_test)
return_vn_train.columns = ["vnindex"]
return_vn_test.columns = ["vnindex"]

returns_port_train =pd.DataFrame(columns=["portpolios"],
                                 data=returns_train[portfolios]@w)

# so sánh với returns của vnindex
pd.concat([returns_port_train,return_vn_train],axis=1).cumsum().plot(figsize = (20,11),
                                                                     title = "Compare cumulative returns");

returns_port_test =pd.DataFrame(columns=["portpolios"],
                                 data=returns_test[portfolios]@w)
# so sánh với returns của vnindex
pd.concat([returns_port_test,return_vn_test],axis=1).cumsum().plot(figsize = (20,8),
                                                                  title = "Compare cumulative returns");

Ticker	AAA	AAT	ABS	ACB	ACC	ACL	ADS	AGG	AGR	ANV	...	VOS	VPB	VPG	VPH	VPI	VRE	VSC	VSH	VTO	YEG
TradingDate
2022-01-04	20.70	15.5148	26.4241	22.9875	16.2744	16.9286	27.0721	36.7089	24.5350	31.8305	...	19.05	22.9097	48.3272	12.9075	49.9372	31.15	34.9553	25.9606	11.2537	25.50
2022-01-06	22.40	15.5584	26.8029	22.5531	18.6018	17.5888	27.0343	37.6897	23.9096	33.0440	...	18.50	22.3378	46.9730	14.7183	52.8800	35.60	34.3124	26.0056	11.3389	24.40
2022-01-07	22.80	15.1226	27.8921	22.2524	19.8864	17.2116	26.9209	38.1101	24.1502	32.9506	...	19.00	21.9882	46.5499	15.7398	53.6825	34.75	34.7946	25.9606	11.3816	23.65
2022-01-10	21.25	14.6432	28.8866	22.0519	21.2660	17.2116	25.0682	39.0908	23.0918	31.2238	...	18.40	21.8611	44.8571	16.8077	52.6124	35.00	34.3124	25.7803	11.0832	22.00
2022-01-11	20.00	14.4689	29.1233	21.8849	22.7408	17.3059	23.7070	41.8230	22.7069	29.8703	...	18.00	21.7976	45.0264	17.9684	51.8099	34.20	33.5089	25.8704	10.6569	20.50
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-11-07	8.72	4.7200	5.2700	22.1000	11.0000	12.4000	12.8500	25.0000	13.5500	27.7500	...	9.46	19.7312	15.3000	7.4500	54.0000	23.15	24.4000	43.3500	9.0100	16.75
2023-11-08	9.29	4.8600	5.4500	22.8000	11.1500	12.5500	13.4000	25.9000	14.4500	29.6500	...	10.05	20.3508	16.3500	7.9700	55.0000	23.70	25.0000	43.1000	9.1500	17.40
2023-11-09	9.21	4.9100	5.6600	22.5500	11.2000	12.6500	13.8000	26.7000	14.9500	29.6500	...	10.05	20.0000	16.7000	8.2500	56.0000	24.30	26.7500	43.6000	9.0200	17.20
2023-11-10	9.10	4.8500	5.6000	22.4000	11.3000	12.5000	13.7000	26.6000	14.9000	29.0000	...	9.90	19.5500	16.2500	8.2500	56.7000	23.95	26.6500	43.5500	9.0500	16.50
2023-11-13	9.12	4.8500	5.5200	22.4500	11.3000	12.4000	13.5000	26.0000	14.8000	28.9500	...	9.85	19.2000	16.7500	8.1800	56.4000	23.50	27.0500	43.7000	9.0500	15.70

Ticker	AAA	AAT	ABS	ACB	ACC	ACL	ADS	AGG	AGR	ANV	...	VOS	VPB	VPG	VPH	VPI	VRE	VSC	VSH	VTO	YEG
Ticker
AAA	1.000000	0.800995	0.925286	0.621155	0.843179	0.640173	0.894249	0.837729	0.855387	0.360453	...	0.833451	0.833812	0.873860	0.917101	0.046521	0.783166	0.564215	-0.306034	0.809277	0.780926
AAT	0.800995	1.000000	0.872650	0.218903	0.767929	0.837582	0.824752	0.820021	0.557446	0.560059	...	0.859875	0.568635	0.909345	0.769936	0.324882	0.549126	0.797596	-0.442349	0.456218	0.869552
ABS	0.925286	0.872650	1.000000	0.376528	0.889446	0.649128	0.858637	0.933118	0.734863	0.274155	...	0.833286	0.713374	0.931276	0.898980	0.205104	0.754311	0.631374	-0.559246	0.684377	0.810823
ACB	0.621155	0.218903	0.376528	1.000000	0.340917	0.244736	0.545067	0.334682	0.773024	0.223521	...	0.404994	0.792058	0.371723	0.591299	-0.396326	0.621569	0.131757	0.266102	0.747304	0.271166
ACC	0.843179	0.767929	0.889446	0.340917	1.000000	0.502891	0.711758	0.801236	0.634632	0.249388	...	0.743277	0.621205	0.777770	0.794516	0.256396	0.622726	0.456402	-0.454469	0.630557	0.753552
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
VRE	0.783166	0.549126	0.754311	0.621569	0.622726	0.408428	0.689090	0.701112	0.695075	0.097110	...	0.554348	0.780027	0.682744	0.738304	-0.026932	1.000000	0.344394	-0.380215	0.715964	0.474823
VSC	0.564215	0.797596	0.631374	0.131757	0.456402	0.867379	0.741603	0.678828	0.389239	0.641634	...	0.720799	0.411365	0.772823	0.566335	0.252062	0.344394	1.000000	-0.207870	0.326632	0.741112
VSH	-0.306034	-0.442349	-0.559246	0.266102	-0.454469	-0.184940	-0.318366	-0.528836	-0.065181	0.325275	...	-0.226182	-0.118665	-0.510829	-0.277537	-0.187729	-0.380215	-0.207870	1.000000	-0.147014	-0.218009
VTO	0.809277	0.456218	0.684377	0.747304	0.630557	0.420525	0.805536	0.665324	0.872062	0.152037	...	0.595179	0.890941	0.671544	0.791985	-0.199332	0.715964	0.326632	-0.147014	1.000000	0.467352
YEG	0.780926	0.869552	0.810823	0.271166	0.753552	0.801225	0.725785	0.805502	0.547680	0.676958	...	0.942562	0.553524	0.801552	0.747124	0.425644	0.474823	0.741112	-0.218009	0.467352	1.000000

Ticker	AAA	AAT	ABS	ACB	ACC	ACL	ADS	AGG	AGR	ANV	...	VOS	VPB	VPG	VPH	VPI	VRE	VSC	VSH	VTO	YEG
Ticker
AAA	0.000000	0.630880	0.386559	0.870454	0.560037	0.848324	0.459893	0.569685	0.537798	1.130971	...	0.577147	0.576520	0.502275	0.407182	1.380927	0.658534	0.933580	1.616190	0.617613	0.661928
AAT	0.630880	0.000000	0.504677	1.249878	0.681279	0.569944	0.592026	0.599965	0.940802	0.938020	...	0.529386	0.928832	0.425804	0.678328	1.161996	0.949604	0.636246	1.698440	1.042863	0.510780
ABS	0.386559	0.504677	0.000000	1.116666	0.470222	0.837702	0.531719	0.365739	0.728199	1.204861	...	0.577432	0.757134	0.370739	0.449488	1.260869	0.700983	0.858634	1.765925	0.794510	0.615105
ACB	0.870454	1.249878	1.116666	0.000000	1.148114	1.229035	0.953869	1.153532	0.673760	1.246178	...	1.090876	0.644890	1.120962	0.904102	1.671123	0.869979	1.317758	1.211526	0.710909	1.207339
ACC	0.560037	0.681279	0.470222	1.148114	0.000000	0.997104	0.759265	0.630499	0.854831	1.225245	...	0.716551	0.870397	0.666679	0.641068	1.219511	0.868647	1.042687	1.705561	0.859585	0.702066
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
VRE	0.658534	0.949604	0.700983	0.869979	0.868647	1.087724	0.788555	0.773160	0.780929	1.343793	...	0.944089	0.663285	0.796563	0.723458	1.433131	0.000000	1.145081	1.661454	0.753705	1.024867
VSC	0.933580	0.636246	0.858634	1.317758	1.042687	0.515017	0.718883	0.801464	1.105225	0.846600	...	0.747263	1.085020	0.674058	0.931306	1.223060	1.145081	0.000000	1.554265	1.160490	0.719566
VSH	1.616190	1.698440	1.765925	1.211526	1.705561	1.539442	1.623802	1.748620	1.459576	1.161658	...	1.566002	1.495771	1.738292	1.598460	1.541252	1.661454	1.554265	0.000000	1.514605	1.560775
VTO	0.617613	1.042863	0.794510	0.710909	0.859585	1.076546	0.623642	0.818140	0.505842	1.302277	...	0.899801	0.467030	0.810501	0.645004	1.548762	0.753705	1.160490	1.514605	0.000000	1.032132
YEG	0.661928	0.510780	0.615105	1.207339	0.702066	0.630515	0.740561	0.623695	0.951126	0.803794	...	0.338934	0.944961	0.629997	0.711163	1.071780	1.024867	0.719566	1.560775	1.032132	0.000000

	AAA	AAT	ABS	ACB	ACC	ACL	ADS	AGG	AGR	ANV	...	VOS	VPB	VPG	VPH	VPI	VRE	VSC	VSH	VTO	YEG
AAA	0.000000	2.037785	0.899128	5.397939	1.369350	1.854991	2.347372	1.696214	3.201172	2.697912	...	2.820441	3.402450	1.007982	2.146925	3.482404	2.144981	2.370008	7.065757	3.107685	3.159375
HQC	0.234630	2.272415	1.133758	5.632570	1.603980	2.089622	2.582002	1.930845	3.435803	2.932543	...	3.055071	3.637080	1.242612	2.381555	3.717035	2.379612	2.604638	7.300387	3.342315	3.394005
FIT	0.251560	1.786225	0.647568	5.146380	1.117790	1.603432	2.095812	1.444655	2.949613	2.446353	...	2.568881	3.150890	0.756422	1.895365	3.230845	1.893422	2.118448	6.814197	2.856125	2.907815
POW	0.376193	2.413977	1.275321	5.774132	1.745542	2.231184	2.723564	2.072407	3.577365	3.074105	...	3.196633	3.778643	1.384175	2.523118	3.858597	2.521174	2.746200	7.441949	3.483877	3.535568
KMR	0.425435	1.612350	0.473693	4.972505	0.943915	1.429557	1.921937	1.270780	2.775738	2.272478	...	2.395006	2.977015	0.582547	1.721490	3.056970	1.719547	1.944573	6.640322	2.682250	2.733940
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
CTF	8.923017	9.173815	8.325346	7.344165	8.795568	9.602731	9.773590	8.832245	5.721845	10.445652	...	9.345624	8.442347	8.755721	8.672109	10.618435	8.368894	10.117747	1.857261	7.122266	9.684559
FIR	9.165357	9.416155	8.567686	7.586505	9.037908	9.845071	10.015930	9.074585	5.964185	10.687992	...	9.587965	8.684687	8.998061	8.914449	10.860775	8.611234	10.360087	2.099601	7.364606	9.926899
KOS	9.561251	9.812049	8.963580	7.982399	9.433802	10.240965	10.411824	9.470479	6.360079	11.083886	...	9.983859	9.080581	9.393955	9.310343	11.256669	9.007128	10.755981	2.495495	7.760500	10.322793
SAB	10.018278	10.269075	9.420607	8.439425	9.890828	10.697991	10.868850	9.927505	6.817105	11.540912	...	10.440885	9.537607	9.850982	9.767369	11.713695	9.464155	11.213008	2.952521	8.217526	10.779819
BCM	10.820987	11.071785	10.223316	9.242134	10.693538	11.500700	11.671559	10.730214	7.619815	12.343621	...	11.243594	10.340316	10.653691	10.570079	12.516404	10.266864	12.015717	3.755230	9.020235	11.582528

	portfolios
TradingDate
2022-01-06	-0.004318
2022-01-07	-0.007422
2022-01-10	-0.002304
2022-01-11	-0.008481
2022-01-12	-0.001866
...	...
2023-10-09	0.003134
2023-10-10	0.006798
2023-10-11	0.009742
2023-10-12	0.006917
2023-10-13	0.000229

1.Import data and data processing¶

2. Filtering¶

3. Modeling: Hierachical clustering¶

4. Stock Portfolio¶

5. Testing¶